{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deep Convolutional GANs\n",
    "\n",
    "In this notebook, you're going to create a GAN using convolutional layers in the generator and discriminator. This is called a Deep Convolutional GAN, or DCGAN for short. The DCGAN architecture was first explored in 2016 and has seen impressive results in generating new images; you can read the [original paper, here](https://arxiv.org/pdf/1511.06434.pdf)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### DCGAN Generator and Discriminator\n",
    "\n",
    "Your first task will be to build the generator and discriminator networks. \n",
    "\n",
    "The Generator is made of a series of transpose convolutional layers, where you typically [batch normalize](https://arxiv.org/pdf/1502.03167.pdf) and then apply an [activation function](https://pytorch.org/docs/stable/nn.html#non-linear-activations-weighted-sum-nonlinearity) after each, except for the last layer where you typically apply a [tanh](https://pytorch.org/docs/stable/generated/torch.nn.Tanh.html#torch.nn.Tanh) or in this case a [sigmoid](https://pytorch.org/docs/stable/generated/torch.nn.Sigmoid.html#torch.nn.Sigmoid) activation. The output should be a $sigmoid$ activated matrix of the same shape as the `input_image` to the discriminator, i.e. $28\\times28\\times1$ since we want to generate grayscale images where each pixel value ranges from 0 to 1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "class Generator(nn.Module):\n",
    "    def __init__(self, latent_dim):\n",
    "        super(Generator, self).__init__()\n",
    "        kernel_size = 5\n",
    "        self.blocks = nn.ModuleList()\n",
    "        self.fc = nn.Linear(latent_dim, 7 * 7 * 128)\n",
    "        self.block1 = nn.Sequential(\n",
    "            nn.BatchNorm2d(128),\n",
    "            nn.ReLU(True),\n",
    "            nn.ConvTranspose2d(128, 128, kernel_size, stride=2, padding=2, output_padding=1),\n",
    "        )\n",
    "        self.block2 = nn.Sequential(\n",
    "            nn.BatchNorm2d(128),\n",
    "            nn.ReLU(True),\n",
    "            nn.ConvTranspose2d(128, 64, kernel_size, stride=2, padding=2, output_padding=1),\n",
    "        )\n",
    "        self.block3 = nn.Sequential(\n",
    "            nn.BatchNorm2d(64),\n",
    "            nn.ReLU(True),\n",
    "            nn.ConvTranspose2d(64, 32, kernel_size, stride=1, padding=2),\n",
    "        )\n",
    "        self.block4 = nn.Sequential(\n",
    "            nn.ConvTranspose2d(32, 1, kernel_size, stride=1, padding=2),\n",
    "            nn.Sigmoid()\n",
    "        )\n",
    "    def forward(self, z):\n",
    "        x = self.fc(z)\n",
    "        x = x.view(-1, 128, 7, 7)\n",
    "        x = self.block1(x)\n",
    "        x = self.block2(x)\n",
    "        x = self.block3(x)\n",
    "        img = self.block4(x)\n",
    "        return img\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Discriminator\n",
    "\n",
    "The discriminator network is a series of [convolutional](https://pytorch.org/docs/stable/nn.html#convolution-layers) layers, where you typically [normalize](https://pytorch.org/docs/stable/nn.html#normalization-layers) and then apply a [Leaky ReLu](https://pytorch.org/docs/stable/generated/torch.nn.LeakyReLU.html#torch.nn.LeakyReLU) activation. The number of output nodes, is equal to 1, which makes sense if you think about it. We want the discriminator to look at an image and output the probability that the image is real or not, i.e. a single scalar value that tells us whether or not the image is real."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Discriminator(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(Discriminator, self).__init__()\n",
    "        kernel_size = 5\n",
    "        layer_filters = [32, 64, 128, 256]\n",
    "        self.block1 = nn.Sequential(\n",
    "            nn.LeakyReLU(0.2),\n",
    "            nn.Conv2d(1, layer_filters[0], kernel_size, stride=2, padding=2),\n",
    "        )\n",
    "        self.block2 = nn.Sequential(\n",
    "            nn.LeakyReLU(0.2),\n",
    "            nn.Conv2d(layer_filters[0], layer_filters[1], kernel_size, stride=2, padding=2),\n",
    "        )\n",
    "        self.block3 = nn.Sequential(\n",
    "            nn.LeakyReLU(0.2),\n",
    "            nn.Conv2d(layer_filters[1], layer_filters[2], kernel_size, stride=2, padding=2),\n",
    "        )\n",
    "        self.block4 = nn.Sequential(\n",
    "            nn.LeakyReLU(0.2),\n",
    "            nn.Conv2d(layer_filters[2], layer_filters[3], kernel_size, stride=1, padding=2),     \n",
    "        )\n",
    "        self.fc = nn.Linear(4 * 4 * layer_filters[3], 1)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.block1(x)\n",
    "        x = self.block2(x)\n",
    "        x = self.block3(x)\n",
    "        x = self.block4(x)\n",
    "        x = torch.flatten(x, 1)\n",
    "        x = self.fc(x)\n",
    "        x = torch.sigmoid(x)\n",
    "        return x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Adversarial Network\n",
    "\n",
    "This network is simply the generator and discriminator networks chained together to create a GAN. The input to the generator, $z$, will be a latent vector that is used to seed the generator, and will be passed to the discriminator, which will ultimately classify the generated image as real or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Adversarial(nn.Module):\n",
    "    def __init__(self, generator, discriminator):\n",
    "        super(Adversarial, self).__init__()\n",
    "        self.generator = generator\n",
    "        self.discriminator = discriminator\n",
    "\n",
    "    def forward(self, z):\n",
    "        img = self.generator(z)\n",
    "        validity = self.discriminator(img)\n",
    "        return validity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dataloaders\n",
    "\n",
    "Using MNIST dataset to train the adversarial networks. Each training batch will contain images with shape $28\\times28\\times1$. We will not use the test dataset since we will train a generative model. You may wish to combine it with the train dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "from torchvision import transforms\n",
    "\n",
    "# Define the transformation to apply to the MNIST dataset\n",
    "transform = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "])\n",
    "\n",
    "# Load the MNIST dataset\n",
    "train_dataset = torchvision.datasets.MNIST(root='~/data', train=True, transform=transform, download=True)\n",
    "#test_dataset = torchvision.datasets.MNIST(root='~/data', train=False, transform=transform, download=True)\n",
    "\n",
    "# Create dataloaders for training and testing\n",
    "batch_size = 64\n",
    "train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "#test_dataloader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, shuffle=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting Generated Images\n",
    "\n",
    "After each epoch, we'll generate and plot some generated images. These images should start off looking like random noise, and then become more recognizable as the network is trained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import torchvision.utils as vutils\n",
    "\n",
    "def save_generated_images(generator, device, epoch=1):\n",
    "    # Generate random noise vectors\n",
    "    noise = torch.randn(16, 100).to(device)\n",
    "    \n",
    "    # Generate images using the generator\n",
    "    with torch.no_grad():\n",
    "        generated_images = generator(noise).detach().cpu()\n",
    "    \n",
    "    # Create a grid of 4 x 4 images\n",
    "    grid = vutils.make_grid(generated_images, nrow=4, padding=2, normalize=True)\n",
    "    \n",
    "    # Save the grid of images\n",
    "    filename = 'gan_ep-%s.png' % (epoch)\n",
    "    vutils.save_image(grid, filename)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### GAN Training\n",
    "\n",
    "After defining the network architectures and dataloader, you can finally train the GAN. The way to train a GAN is by training the discriminator and adversarial networks in an alternating fashion, where we first update the discriminator a set number of times, then update the adversarial network. \n",
    "\n",
    "During training of the discriminator, the generator's parameters are frozen, and vice versa when training the generator. The learning rate for training the discriminator is twice the learning rate of the generator. We want the discriminator to learn but at the same time we don't want it to be completely accurate in its prediction to give chance for the generator to learn and improve."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def train(generator, discriminator, \n",
    "          adversarial, trainloader,\n",
    "          epochs=50):\n",
    "    generator.train()\n",
    "    discriminator.train()\n",
    "    criterion = nn.BCELoss()\n",
    "    lr = 2e-4\n",
    "    decay = 6e-8\n",
    "    factor = 0.5\n",
    "    optimizer_g = torch.optim.RMSprop(generator.parameters(), \n",
    "                                    lr=lr*factor, weight_decay=decay*factor)    \n",
    "    optimizer_d = torch.optim.RMSprop(discriminator.parameters(),\n",
    "                                    lr=lr, weight_decay=decay) \n",
    "    # lr scheduler is not needed but you can try it\n",
    "    #scheduler_g = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer_g, T_max=epochs)\n",
    "    #scheduler_d = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer_d, T_max=epochs)\n",
    "    \n",
    "    for epoch in range(epochs):\n",
    "        for i, (real_imgs, _) in enumerate(trainloader):\n",
    "            real_imgs = real_imgs.to(device)\n",
    "            batch_size = real_imgs.shape[0]\n",
    "            real_labels = torch.ones(batch_size, 1, requires_grad=False).to(device)\n",
    "            fake_labels = torch.zeros(batch_size, 1, requires_grad=False).to(device)\n",
    "            z = torch.randn(batch_size, 100).to(device)\n",
    "            \n",
    "            # Train discriminator\n",
    "            optimizer_d.zero_grad()\n",
    "            generator.eval()\n",
    "            with torch.no_grad():\n",
    "                fake_imgs = generator(z)\n",
    "        \n",
    "            real_out = discriminator(real_imgs)\n",
    "            fake_out = discriminator(fake_imgs)\n",
    "            loss_real = criterion(real_out, real_labels)\n",
    "            loss_fake = criterion(fake_out, fake_labels)\n",
    "            loss_d = loss_real + loss_fake\n",
    "            loss_d.backward()\n",
    "            optimizer_d.step()\n",
    "            \n",
    "            # Train generator\n",
    "            optimizer_g.zero_grad()\n",
    "            generator.train()\n",
    "            z = torch.randn(batch_size, 100, requires_grad=False).to(device)\n",
    "            \n",
    "            fake_out = adversarial(z)\n",
    "            loss_g = criterion(fake_out, real_labels)\n",
    "            loss_g.backward()\n",
    "            optimizer_g.step()\n",
    "            if i % 100 == 0:\n",
    "                print('Epoch: %d, Iter: %d, Loss D: %.6f, Loss G: %.6f' % (epoch, i, loss_d.item(), loss_g.item()))\n",
    "        #scheduler_g.step()\n",
    "        #scheduler_d.step()\n",
    "        print('Learning Rate (Generator):', optimizer_g.param_groups[0]['lr'])\n",
    "        print('Learning Rate (Discriminator):', optimizer_d.param_groups[0]['lr'])\n",
    "        save_generated_images(generator, device, epoch=epoch)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the GAN\n",
    "\n",
    "We instantiate a generator, a discriminator and an adversarial network. Finally, we train the GAN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0, Iter: 0, Loss D: 1.388577, Loss G: 0.999602\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 0, Iter: 100, Loss D: 0.593850, Loss G: 1.532923\n",
      "Epoch: 0, Iter: 200, Loss D: 1.049684, Loss G: 0.389821\n",
      "Epoch: 0, Iter: 300, Loss D: 1.161914, Loss G: 0.913161\n",
      "Epoch: 0, Iter: 400, Loss D: 1.159736, Loss G: 0.907335\n",
      "Epoch: 0, Iter: 500, Loss D: 0.994731, Loss G: 1.025943\n",
      "Epoch: 0, Iter: 600, Loss D: 1.085476, Loss G: 0.873054\n",
      "Epoch: 0, Iter: 700, Loss D: 1.287905, Loss G: 0.672095\n",
      "Epoch: 0, Iter: 800, Loss D: 0.874986, Loss G: 1.268867\n",
      "Epoch: 0, Iter: 900, Loss D: 1.124874, Loss G: 0.866482\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 1, Iter: 0, Loss D: 0.886704, Loss G: 1.287068\n",
      "Epoch: 1, Iter: 100, Loss D: 1.164963, Loss G: 0.741889\n",
      "Epoch: 1, Iter: 200, Loss D: 0.859661, Loss G: 1.492760\n",
      "Epoch: 1, Iter: 300, Loss D: 0.844474, Loss G: 1.875231\n",
      "Epoch: 1, Iter: 400, Loss D: 1.155619, Loss G: 1.089379\n",
      "Epoch: 1, Iter: 500, Loss D: 1.018634, Loss G: 1.127913\n",
      "Epoch: 1, Iter: 600, Loss D: 0.876486, Loss G: 1.248156\n",
      "Epoch: 1, Iter: 700, Loss D: 1.009245, Loss G: 1.146368\n",
      "Epoch: 1, Iter: 800, Loss D: 0.835290, Loss G: 1.079102\n",
      "Epoch: 1, Iter: 900, Loss D: 0.907067, Loss G: 1.482624\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 2, Iter: 0, Loss D: 0.963135, Loss G: 1.815355\n",
      "Epoch: 2, Iter: 100, Loss D: 0.613132, Loss G: 1.340900\n",
      "Epoch: 2, Iter: 200, Loss D: 0.985345, Loss G: 2.504944\n",
      "Epoch: 2, Iter: 300, Loss D: 0.746126, Loss G: 2.476890\n",
      "Epoch: 2, Iter: 400, Loss D: 0.811030, Loss G: 1.953310\n",
      "Epoch: 2, Iter: 500, Loss D: 0.680287, Loss G: 1.545724\n",
      "Epoch: 2, Iter: 600, Loss D: 0.739756, Loss G: 1.333550\n",
      "Epoch: 2, Iter: 700, Loss D: 0.715500, Loss G: 2.015961\n",
      "Epoch: 2, Iter: 800, Loss D: 0.889774, Loss G: 1.500262\n",
      "Epoch: 2, Iter: 900, Loss D: 0.829664, Loss G: 1.931390\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 3, Iter: 0, Loss D: 0.796473, Loss G: 2.339442\n",
      "Epoch: 3, Iter: 100, Loss D: 0.803732, Loss G: 1.323882\n",
      "Epoch: 3, Iter: 200, Loss D: 0.619892, Loss G: 1.404459\n",
      "Epoch: 3, Iter: 300, Loss D: 0.714007, Loss G: 1.644360\n",
      "Epoch: 3, Iter: 400, Loss D: 0.756184, Loss G: 2.261940\n",
      "Epoch: 3, Iter: 500, Loss D: 0.750196, Loss G: 2.009754\n",
      "Epoch: 3, Iter: 600, Loss D: 0.822610, Loss G: 1.976577\n",
      "Epoch: 3, Iter: 700, Loss D: 0.782874, Loss G: 1.628135\n",
      "Epoch: 3, Iter: 800, Loss D: 0.811330, Loss G: 1.588498\n",
      "Epoch: 3, Iter: 900, Loss D: 1.001350, Loss G: 1.355653\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 4, Iter: 0, Loss D: 1.060743, Loss G: 2.586688\n",
      "Epoch: 4, Iter: 100, Loss D: 0.832694, Loss G: 2.479385\n",
      "Epoch: 4, Iter: 200, Loss D: 0.727874, Loss G: 1.856073\n",
      "Epoch: 4, Iter: 300, Loss D: 0.908050, Loss G: 2.087047\n",
      "Epoch: 4, Iter: 400, Loss D: 0.960148, Loss G: 0.822554\n",
      "Epoch: 4, Iter: 500, Loss D: 0.734969, Loss G: 2.030509\n",
      "Epoch: 4, Iter: 600, Loss D: 0.965731, Loss G: 1.108250\n",
      "Epoch: 4, Iter: 700, Loss D: 0.690213, Loss G: 1.512181\n",
      "Epoch: 4, Iter: 800, Loss D: 0.848418, Loss G: 2.342751\n",
      "Epoch: 4, Iter: 900, Loss D: 0.931078, Loss G: 2.187742\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 5, Iter: 0, Loss D: 0.886420, Loss G: 1.822713\n",
      "Epoch: 5, Iter: 100, Loss D: 0.845902, Loss G: 2.607379\n",
      "Epoch: 5, Iter: 200, Loss D: 0.872177, Loss G: 2.409261\n",
      "Epoch: 5, Iter: 300, Loss D: 0.932078, Loss G: 1.851708\n",
      "Epoch: 5, Iter: 400, Loss D: 0.849143, Loss G: 1.774244\n",
      "Epoch: 5, Iter: 500, Loss D: 0.724095, Loss G: 1.557511\n",
      "Epoch: 5, Iter: 600, Loss D: 0.859408, Loss G: 1.436986\n",
      "Epoch: 5, Iter: 700, Loss D: 0.817314, Loss G: 1.294903\n",
      "Epoch: 5, Iter: 800, Loss D: 0.843448, Loss G: 1.459432\n",
      "Epoch: 5, Iter: 900, Loss D: 0.742337, Loss G: 1.864476\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 6, Iter: 0, Loss D: 0.781240, Loss G: 1.748958\n",
      "Epoch: 6, Iter: 100, Loss D: 0.958078, Loss G: 1.293955\n",
      "Epoch: 6, Iter: 200, Loss D: 0.834834, Loss G: 1.410412\n",
      "Epoch: 6, Iter: 300, Loss D: 0.747481, Loss G: 1.803894\n",
      "Epoch: 6, Iter: 400, Loss D: 0.753124, Loss G: 1.847888\n",
      "Epoch: 6, Iter: 500, Loss D: 0.881554, Loss G: 1.662139\n",
      "Epoch: 6, Iter: 600, Loss D: 0.843236, Loss G: 1.889440\n",
      "Epoch: 6, Iter: 700, Loss D: 0.882645, Loss G: 1.818894\n",
      "Epoch: 6, Iter: 800, Loss D: 0.845401, Loss G: 2.225929\n",
      "Epoch: 6, Iter: 900, Loss D: 0.864376, Loss G: 1.328719\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 7, Iter: 0, Loss D: 0.979897, Loss G: 2.340914\n",
      "Epoch: 7, Iter: 100, Loss D: 0.711735, Loss G: 2.222147\n",
      "Epoch: 7, Iter: 200, Loss D: 0.748545, Loss G: 1.523969\n",
      "Epoch: 7, Iter: 300, Loss D: 0.663567, Loss G: 1.596684\n",
      "Epoch: 7, Iter: 400, Loss D: 0.812970, Loss G: 1.078649\n",
      "Epoch: 7, Iter: 500, Loss D: 0.588268, Loss G: 2.146947\n",
      "Epoch: 7, Iter: 600, Loss D: 0.808992, Loss G: 1.878764\n",
      "Epoch: 7, Iter: 700, Loss D: 0.789376, Loss G: 2.354690\n",
      "Epoch: 7, Iter: 800, Loss D: 0.810040, Loss G: 1.964968\n",
      "Epoch: 7, Iter: 900, Loss D: 0.975511, Loss G: 1.197486\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 8, Iter: 0, Loss D: 0.831956, Loss G: 2.290230\n",
      "Epoch: 8, Iter: 100, Loss D: 0.750695, Loss G: 1.679041\n",
      "Epoch: 8, Iter: 200, Loss D: 0.739790, Loss G: 1.731807\n",
      "Epoch: 8, Iter: 300, Loss D: 0.789991, Loss G: 1.262952\n",
      "Epoch: 8, Iter: 400, Loss D: 0.711980, Loss G: 2.154853\n",
      "Epoch: 8, Iter: 500, Loss D: 0.720574, Loss G: 1.671681\n",
      "Epoch: 8, Iter: 600, Loss D: 0.728529, Loss G: 1.953596\n",
      "Epoch: 8, Iter: 700, Loss D: 0.779141, Loss G: 1.903747\n",
      "Epoch: 8, Iter: 800, Loss D: 0.965172, Loss G: 1.246647\n",
      "Epoch: 8, Iter: 900, Loss D: 0.780291, Loss G: 2.313226\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 9, Iter: 0, Loss D: 0.851618, Loss G: 2.469453\n",
      "Epoch: 9, Iter: 100, Loss D: 0.698211, Loss G: 1.609808\n",
      "Epoch: 9, Iter: 200, Loss D: 1.024509, Loss G: 2.377007\n",
      "Epoch: 9, Iter: 300, Loss D: 0.883738, Loss G: 2.083955\n",
      "Epoch: 9, Iter: 400, Loss D: 0.831026, Loss G: 1.358099\n",
      "Epoch: 9, Iter: 500, Loss D: 0.786296, Loss G: 1.927656\n",
      "Epoch: 9, Iter: 600, Loss D: 0.780878, Loss G: 1.417851\n",
      "Epoch: 9, Iter: 700, Loss D: 0.966455, Loss G: 1.749246\n",
      "Epoch: 9, Iter: 800, Loss D: 0.807876, Loss G: 1.702178\n",
      "Epoch: 9, Iter: 900, Loss D: 0.880731, Loss G: 1.509746\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 10, Iter: 0, Loss D: 0.901019, Loss G: 1.545577\n",
      "Epoch: 10, Iter: 100, Loss D: 0.853245, Loss G: 1.648852\n",
      "Epoch: 10, Iter: 200, Loss D: 0.920287, Loss G: 1.574677\n",
      "Epoch: 10, Iter: 300, Loss D: 0.955912, Loss G: 1.077305\n",
      "Epoch: 10, Iter: 400, Loss D: 0.877018, Loss G: 1.951629\n",
      "Epoch: 10, Iter: 500, Loss D: 0.842296, Loss G: 1.651445\n",
      "Epoch: 10, Iter: 600, Loss D: 0.843753, Loss G: 1.794404\n",
      "Epoch: 10, Iter: 700, Loss D: 0.796909, Loss G: 1.619569\n",
      "Epoch: 10, Iter: 800, Loss D: 0.905369, Loss G: 1.550742\n",
      "Epoch: 10, Iter: 900, Loss D: 0.853423, Loss G: 1.583090\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 11, Iter: 0, Loss D: 0.929712, Loss G: 2.422639\n",
      "Epoch: 11, Iter: 100, Loss D: 0.806574, Loss G: 1.793682\n",
      "Epoch: 11, Iter: 200, Loss D: 0.751154, Loss G: 1.841135\n",
      "Epoch: 11, Iter: 300, Loss D: 0.733744, Loss G: 1.803857\n",
      "Epoch: 11, Iter: 400, Loss D: 0.832005, Loss G: 1.602287\n",
      "Epoch: 11, Iter: 500, Loss D: 0.961398, Loss G: 1.572798\n",
      "Epoch: 11, Iter: 600, Loss D: 0.787162, Loss G: 1.696089\n",
      "Epoch: 11, Iter: 700, Loss D: 0.754400, Loss G: 1.947362\n",
      "Epoch: 11, Iter: 800, Loss D: 0.765046, Loss G: 1.738448\n",
      "Epoch: 11, Iter: 900, Loss D: 0.879842, Loss G: 1.879013\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 12, Iter: 0, Loss D: 0.801512, Loss G: 1.547260\n",
      "Epoch: 12, Iter: 100, Loss D: 0.982565, Loss G: 2.528060\n",
      "Epoch: 12, Iter: 200, Loss D: 0.942095, Loss G: 1.476384\n",
      "Epoch: 12, Iter: 300, Loss D: 0.761069, Loss G: 1.947415\n",
      "Epoch: 12, Iter: 400, Loss D: 0.832160, Loss G: 1.621591\n",
      "Epoch: 12, Iter: 500, Loss D: 0.727010, Loss G: 2.071482\n",
      "Epoch: 12, Iter: 600, Loss D: 0.983023, Loss G: 1.098821\n",
      "Epoch: 12, Iter: 700, Loss D: 0.938703, Loss G: 1.396298\n",
      "Epoch: 12, Iter: 800, Loss D: 0.806678, Loss G: 1.324788\n",
      "Epoch: 12, Iter: 900, Loss D: 1.027369, Loss G: 1.558897\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 13, Iter: 0, Loss D: 0.900560, Loss G: 1.926109\n",
      "Epoch: 13, Iter: 100, Loss D: 0.855928, Loss G: 1.853679\n",
      "Epoch: 13, Iter: 200, Loss D: 0.798432, Loss G: 2.143711\n",
      "Epoch: 13, Iter: 300, Loss D: 0.996176, Loss G: 1.614932\n",
      "Epoch: 13, Iter: 400, Loss D: 0.985302, Loss G: 1.447757\n",
      "Epoch: 13, Iter: 500, Loss D: 0.849993, Loss G: 1.242497\n",
      "Epoch: 13, Iter: 600, Loss D: 0.882065, Loss G: 1.517354\n",
      "Epoch: 13, Iter: 700, Loss D: 1.114660, Loss G: 1.857732\n",
      "Epoch: 13, Iter: 800, Loss D: 1.038657, Loss G: 1.215250\n",
      "Epoch: 13, Iter: 900, Loss D: 0.882993, Loss G: 1.693008\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 14, Iter: 0, Loss D: 1.097628, Loss G: 1.568050\n",
      "Epoch: 14, Iter: 100, Loss D: 0.783583, Loss G: 2.003935\n",
      "Epoch: 14, Iter: 200, Loss D: 0.814241, Loss G: 1.615082\n",
      "Epoch: 14, Iter: 300, Loss D: 0.798990, Loss G: 1.533846\n",
      "Epoch: 14, Iter: 400, Loss D: 0.836895, Loss G: 1.734852\n",
      "Epoch: 14, Iter: 500, Loss D: 0.853815, Loss G: 1.731119\n",
      "Epoch: 14, Iter: 600, Loss D: 0.904993, Loss G: 1.622187\n",
      "Epoch: 14, Iter: 700, Loss D: 0.754381, Loss G: 1.709707\n",
      "Epoch: 14, Iter: 800, Loss D: 0.882468, Loss G: 1.414665\n",
      "Epoch: 14, Iter: 900, Loss D: 0.852184, Loss G: 1.819553\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 15, Iter: 0, Loss D: 0.900458, Loss G: 1.529396\n",
      "Epoch: 15, Iter: 100, Loss D: 0.930215, Loss G: 1.722856\n",
      "Epoch: 15, Iter: 200, Loss D: 0.762323, Loss G: 2.362325\n",
      "Epoch: 15, Iter: 300, Loss D: 0.999279, Loss G: 1.328819\n",
      "Epoch: 15, Iter: 400, Loss D: 0.822907, Loss G: 1.538455\n",
      "Epoch: 15, Iter: 500, Loss D: 0.806040, Loss G: 1.558833\n",
      "Epoch: 15, Iter: 600, Loss D: 0.946303, Loss G: 1.246830\n",
      "Epoch: 15, Iter: 700, Loss D: 0.808437, Loss G: 1.855124\n",
      "Epoch: 15, Iter: 800, Loss D: 0.864322, Loss G: 1.467776\n",
      "Epoch: 15, Iter: 900, Loss D: 0.973714, Loss G: 1.203756\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 16, Iter: 0, Loss D: 0.895320, Loss G: 1.800325\n",
      "Epoch: 16, Iter: 100, Loss D: 1.056804, Loss G: 2.095987\n",
      "Epoch: 16, Iter: 200, Loss D: 0.873131, Loss G: 1.910633\n",
      "Epoch: 16, Iter: 300, Loss D: 0.867498, Loss G: 1.822608\n",
      "Epoch: 16, Iter: 400, Loss D: 0.820767, Loss G: 1.620546\n",
      "Epoch: 16, Iter: 500, Loss D: 0.718589, Loss G: 1.543291\n",
      "Epoch: 16, Iter: 600, Loss D: 0.938208, Loss G: 1.799062\n",
      "Epoch: 16, Iter: 700, Loss D: 0.900530, Loss G: 2.057087\n",
      "Epoch: 16, Iter: 800, Loss D: 0.842857, Loss G: 1.775660\n",
      "Epoch: 16, Iter: 900, Loss D: 0.735270, Loss G: 1.601007\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 17, Iter: 0, Loss D: 0.909946, Loss G: 1.718192\n",
      "Epoch: 17, Iter: 100, Loss D: 0.716966, Loss G: 1.465542\n",
      "Epoch: 17, Iter: 200, Loss D: 0.835176, Loss G: 1.836682\n",
      "Epoch: 17, Iter: 300, Loss D: 0.677295, Loss G: 1.657869\n",
      "Epoch: 17, Iter: 400, Loss D: 0.836549, Loss G: 2.119239\n",
      "Epoch: 17, Iter: 500, Loss D: 0.762074, Loss G: 2.014612\n",
      "Epoch: 17, Iter: 600, Loss D: 0.772002, Loss G: 1.930818\n",
      "Epoch: 17, Iter: 700, Loss D: 0.996210, Loss G: 1.766042\n",
      "Epoch: 17, Iter: 800, Loss D: 0.774923, Loss G: 1.865088\n",
      "Epoch: 17, Iter: 900, Loss D: 1.013849, Loss G: 2.176671\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 18, Iter: 0, Loss D: 0.993049, Loss G: 2.485153\n",
      "Epoch: 18, Iter: 100, Loss D: 0.869773, Loss G: 2.069819\n",
      "Epoch: 18, Iter: 200, Loss D: 1.136528, Loss G: 0.834437\n",
      "Epoch: 18, Iter: 300, Loss D: 0.922764, Loss G: 2.109701\n",
      "Epoch: 18, Iter: 400, Loss D: 1.000975, Loss G: 2.000174\n",
      "Epoch: 18, Iter: 500, Loss D: 0.802665, Loss G: 2.113297\n",
      "Epoch: 18, Iter: 600, Loss D: 0.817313, Loss G: 1.923413\n",
      "Epoch: 18, Iter: 700, Loss D: 0.857832, Loss G: 1.243257\n",
      "Epoch: 18, Iter: 800, Loss D: 0.917198, Loss G: 2.260765\n",
      "Epoch: 18, Iter: 900, Loss D: 0.881379, Loss G: 1.860928\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 19, Iter: 0, Loss D: 1.006250, Loss G: 2.279591\n",
      "Epoch: 19, Iter: 100, Loss D: 1.053788, Loss G: 1.919575\n",
      "Epoch: 19, Iter: 200, Loss D: 0.721165, Loss G: 1.840610\n",
      "Epoch: 19, Iter: 300, Loss D: 0.973274, Loss G: 1.770656\n",
      "Epoch: 19, Iter: 400, Loss D: 0.857807, Loss G: 1.436446\n",
      "Epoch: 19, Iter: 500, Loss D: 0.949994, Loss G: 1.041109\n",
      "Epoch: 19, Iter: 600, Loss D: 0.760418, Loss G: 2.106503\n",
      "Epoch: 19, Iter: 700, Loss D: 0.719184, Loss G: 2.344705\n",
      "Epoch: 19, Iter: 800, Loss D: 0.811685, Loss G: 2.001289\n",
      "Epoch: 19, Iter: 900, Loss D: 0.933352, Loss G: 1.061492\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 20, Iter: 0, Loss D: 0.683428, Loss G: 1.480351\n",
      "Epoch: 20, Iter: 100, Loss D: 0.851063, Loss G: 1.844134\n",
      "Epoch: 20, Iter: 200, Loss D: 0.929434, Loss G: 1.841490\n",
      "Epoch: 20, Iter: 300, Loss D: 0.917227, Loss G: 2.059394\n",
      "Epoch: 20, Iter: 400, Loss D: 0.726788, Loss G: 1.804140\n",
      "Epoch: 20, Iter: 500, Loss D: 0.748085, Loss G: 2.073308\n",
      "Epoch: 20, Iter: 600, Loss D: 0.948688, Loss G: 1.324113\n",
      "Epoch: 20, Iter: 700, Loss D: 1.019222, Loss G: 1.527302\n",
      "Epoch: 20, Iter: 800, Loss D: 0.950634, Loss G: 2.272203\n",
      "Epoch: 20, Iter: 900, Loss D: 0.769193, Loss G: 1.508798\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 21, Iter: 0, Loss D: 0.732653, Loss G: 1.907919\n",
      "Epoch: 21, Iter: 100, Loss D: 0.807083, Loss G: 1.610263\n",
      "Epoch: 21, Iter: 200, Loss D: 0.761002, Loss G: 1.783996\n",
      "Epoch: 21, Iter: 300, Loss D: 0.932618, Loss G: 1.370517\n",
      "Epoch: 21, Iter: 400, Loss D: 1.191380, Loss G: 1.405788\n",
      "Epoch: 21, Iter: 500, Loss D: 1.092740, Loss G: 2.214053\n",
      "Epoch: 21, Iter: 600, Loss D: 0.815734, Loss G: 1.730375\n",
      "Epoch: 21, Iter: 700, Loss D: 0.933428, Loss G: 1.729662\n",
      "Epoch: 21, Iter: 800, Loss D: 0.922233, Loss G: 1.323047\n",
      "Epoch: 21, Iter: 900, Loss D: 0.805488, Loss G: 1.845437\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 22, Iter: 0, Loss D: 0.926322, Loss G: 2.049479\n",
      "Epoch: 22, Iter: 100, Loss D: 0.798251, Loss G: 1.321804\n",
      "Epoch: 22, Iter: 200, Loss D: 0.668979, Loss G: 2.255472\n",
      "Epoch: 22, Iter: 300, Loss D: 0.786693, Loss G: 2.107697\n",
      "Epoch: 22, Iter: 400, Loss D: 0.839242, Loss G: 2.075249\n",
      "Epoch: 22, Iter: 500, Loss D: 1.003532, Loss G: 1.687360\n",
      "Epoch: 22, Iter: 600, Loss D: 0.733526, Loss G: 1.995513\n",
      "Epoch: 22, Iter: 700, Loss D: 0.855913, Loss G: 1.291161\n",
      "Epoch: 22, Iter: 800, Loss D: 0.765603, Loss G: 1.382149\n",
      "Epoch: 22, Iter: 900, Loss D: 0.739182, Loss G: 1.474240\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 23, Iter: 0, Loss D: 0.858704, Loss G: 2.124519\n",
      "Epoch: 23, Iter: 100, Loss D: 1.036190, Loss G: 1.136769\n",
      "Epoch: 23, Iter: 200, Loss D: 0.767925, Loss G: 1.465068\n",
      "Epoch: 23, Iter: 300, Loss D: 0.787736, Loss G: 1.891140\n",
      "Epoch: 23, Iter: 400, Loss D: 0.788072, Loss G: 1.626459\n",
      "Epoch: 23, Iter: 500, Loss D: 0.872174, Loss G: 1.646036\n",
      "Epoch: 23, Iter: 600, Loss D: 0.818478, Loss G: 2.187718\n",
      "Epoch: 23, Iter: 700, Loss D: 0.921300, Loss G: 1.368806\n",
      "Epoch: 23, Iter: 800, Loss D: 0.881224, Loss G: 1.936077\n",
      "Epoch: 23, Iter: 900, Loss D: 0.866788, Loss G: 2.097085\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 24, Iter: 0, Loss D: 0.903385, Loss G: 1.938099\n",
      "Epoch: 24, Iter: 100, Loss D: 0.942027, Loss G: 2.277581\n",
      "Epoch: 24, Iter: 200, Loss D: 0.751378, Loss G: 1.628212\n",
      "Epoch: 24, Iter: 300, Loss D: 0.788329, Loss G: 2.171789\n",
      "Epoch: 24, Iter: 400, Loss D: 0.928527, Loss G: 1.673345\n",
      "Epoch: 24, Iter: 500, Loss D: 0.951027, Loss G: 1.696211\n",
      "Epoch: 24, Iter: 600, Loss D: 1.097725, Loss G: 2.173717\n",
      "Epoch: 24, Iter: 700, Loss D: 0.953477, Loss G: 1.797379\n",
      "Epoch: 24, Iter: 800, Loss D: 0.783237, Loss G: 1.670236\n",
      "Epoch: 24, Iter: 900, Loss D: 0.806649, Loss G: 1.271392\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 25, Iter: 0, Loss D: 1.059350, Loss G: 2.053620\n",
      "Epoch: 25, Iter: 100, Loss D: 0.706337, Loss G: 1.889511\n",
      "Epoch: 25, Iter: 200, Loss D: 0.682882, Loss G: 1.792458\n",
      "Epoch: 25, Iter: 300, Loss D: 0.790268, Loss G: 1.569044\n",
      "Epoch: 25, Iter: 400, Loss D: 0.807642, Loss G: 1.424905\n",
      "Epoch: 25, Iter: 500, Loss D: 0.794877, Loss G: 1.820418\n",
      "Epoch: 25, Iter: 600, Loss D: 0.917309, Loss G: 1.568767\n",
      "Epoch: 25, Iter: 700, Loss D: 0.787108, Loss G: 1.718253\n",
      "Epoch: 25, Iter: 800, Loss D: 0.880758, Loss G: 1.825660\n",
      "Epoch: 25, Iter: 900, Loss D: 0.740009, Loss G: 1.845586\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 26, Iter: 0, Loss D: 0.818842, Loss G: 1.992791\n",
      "Epoch: 26, Iter: 100, Loss D: 0.707258, Loss G: 1.860228\n",
      "Epoch: 26, Iter: 200, Loss D: 0.921574, Loss G: 1.740119\n",
      "Epoch: 26, Iter: 300, Loss D: 0.846385, Loss G: 1.893710\n",
      "Epoch: 26, Iter: 400, Loss D: 0.845511, Loss G: 1.695045\n",
      "Epoch: 26, Iter: 500, Loss D: 0.736611, Loss G: 1.384080\n",
      "Epoch: 26, Iter: 600, Loss D: 0.784385, Loss G: 2.071875\n",
      "Epoch: 26, Iter: 700, Loss D: 0.912594, Loss G: 1.826926\n",
      "Epoch: 26, Iter: 800, Loss D: 0.772628, Loss G: 1.792528\n",
      "Epoch: 26, Iter: 900, Loss D: 0.909766, Loss G: 1.848718\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 27, Iter: 0, Loss D: 0.737034, Loss G: 1.989022\n",
      "Epoch: 27, Iter: 100, Loss D: 0.820706, Loss G: 1.918690\n",
      "Epoch: 27, Iter: 200, Loss D: 0.811401, Loss G: 2.073843\n",
      "Epoch: 27, Iter: 300, Loss D: 0.852047, Loss G: 1.412913\n",
      "Epoch: 27, Iter: 400, Loss D: 0.959421, Loss G: 1.653608\n",
      "Epoch: 27, Iter: 500, Loss D: 0.719405, Loss G: 2.852684\n",
      "Epoch: 27, Iter: 600, Loss D: 0.959716, Loss G: 1.948184\n",
      "Epoch: 27, Iter: 700, Loss D: 0.680836, Loss G: 1.858453\n",
      "Epoch: 27, Iter: 800, Loss D: 0.732890, Loss G: 1.709356\n",
      "Epoch: 27, Iter: 900, Loss D: 0.738263, Loss G: 1.820106\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 28, Iter: 0, Loss D: 0.800369, Loss G: 1.916853\n",
      "Epoch: 28, Iter: 100, Loss D: 0.816946, Loss G: 1.382982\n",
      "Epoch: 28, Iter: 200, Loss D: 0.844353, Loss G: 2.063740\n",
      "Epoch: 28, Iter: 300, Loss D: 0.847327, Loss G: 1.770423\n",
      "Epoch: 28, Iter: 400, Loss D: 0.797694, Loss G: 1.482235\n",
      "Epoch: 28, Iter: 500, Loss D: 0.790393, Loss G: 1.713854\n",
      "Epoch: 28, Iter: 600, Loss D: 0.917455, Loss G: 1.246886\n",
      "Epoch: 28, Iter: 700, Loss D: 0.917536, Loss G: 1.455918\n",
      "Epoch: 28, Iter: 800, Loss D: 0.876242, Loss G: 1.745376\n",
      "Epoch: 28, Iter: 900, Loss D: 0.992861, Loss G: 1.392637\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 29, Iter: 0, Loss D: 0.699381, Loss G: 1.741590\n",
      "Epoch: 29, Iter: 100, Loss D: 0.804929, Loss G: 1.897536\n",
      "Epoch: 29, Iter: 200, Loss D: 0.747150, Loss G: 2.149176\n",
      "Epoch: 29, Iter: 300, Loss D: 0.816944, Loss G: 2.438618\n",
      "Epoch: 29, Iter: 400, Loss D: 0.891004, Loss G: 1.911301\n",
      "Epoch: 29, Iter: 500, Loss D: 1.258931, Loss G: 1.476459\n",
      "Epoch: 29, Iter: 600, Loss D: 0.729669, Loss G: 2.332212\n",
      "Epoch: 29, Iter: 700, Loss D: 0.699422, Loss G: 2.145059\n",
      "Epoch: 29, Iter: 800, Loss D: 0.872114, Loss G: 2.167689\n",
      "Epoch: 29, Iter: 900, Loss D: 0.890387, Loss G: 1.908730\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 30, Iter: 0, Loss D: 0.835649, Loss G: 1.470419\n",
      "Epoch: 30, Iter: 100, Loss D: 0.744113, Loss G: 1.952349\n",
      "Epoch: 30, Iter: 200, Loss D: 0.735299, Loss G: 1.918014\n",
      "Epoch: 30, Iter: 300, Loss D: 0.727465, Loss G: 2.251785\n",
      "Epoch: 30, Iter: 400, Loss D: 0.775230, Loss G: 1.845224\n",
      "Epoch: 30, Iter: 500, Loss D: 0.969002, Loss G: 1.591544\n",
      "Epoch: 30, Iter: 600, Loss D: 0.959430, Loss G: 1.681205\n",
      "Epoch: 30, Iter: 700, Loss D: 0.966638, Loss G: 1.605354\n",
      "Epoch: 30, Iter: 800, Loss D: 0.801694, Loss G: 2.141674\n",
      "Epoch: 30, Iter: 900, Loss D: 0.795814, Loss G: 1.968325\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 31, Iter: 0, Loss D: 0.771831, Loss G: 1.992340\n",
      "Epoch: 31, Iter: 100, Loss D: 0.856672, Loss G: 2.470421\n",
      "Epoch: 31, Iter: 200, Loss D: 0.662262, Loss G: 2.319604\n",
      "Epoch: 31, Iter: 300, Loss D: 0.792507, Loss G: 2.261339\n",
      "Epoch: 31, Iter: 400, Loss D: 0.749512, Loss G: 1.972816\n",
      "Epoch: 31, Iter: 500, Loss D: 0.869025, Loss G: 1.869268\n",
      "Epoch: 31, Iter: 600, Loss D: 0.892427, Loss G: 1.948523\n",
      "Epoch: 31, Iter: 700, Loss D: 0.896113, Loss G: 1.927970\n",
      "Epoch: 31, Iter: 800, Loss D: 0.787749, Loss G: 2.034936\n",
      "Epoch: 31, Iter: 900, Loss D: 0.800213, Loss G: 2.053278\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 32, Iter: 0, Loss D: 0.705508, Loss G: 1.447079\n",
      "Epoch: 32, Iter: 100, Loss D: 0.807437, Loss G: 2.097281\n",
      "Epoch: 32, Iter: 200, Loss D: 0.747158, Loss G: 2.040128\n",
      "Epoch: 32, Iter: 300, Loss D: 0.774377, Loss G: 1.335367\n",
      "Epoch: 32, Iter: 400, Loss D: 0.847888, Loss G: 1.661975\n",
      "Epoch: 32, Iter: 500, Loss D: 1.007897, Loss G: 1.552064\n",
      "Epoch: 32, Iter: 600, Loss D: 0.827569, Loss G: 2.249355\n",
      "Epoch: 32, Iter: 700, Loss D: 1.122908, Loss G: 0.950326\n",
      "Epoch: 32, Iter: 800, Loss D: 0.736033, Loss G: 2.335516\n",
      "Epoch: 32, Iter: 900, Loss D: 0.965553, Loss G: 1.824126\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 33, Iter: 0, Loss D: 0.586695, Loss G: 1.804992\n",
      "Epoch: 33, Iter: 100, Loss D: 0.856212, Loss G: 2.227151\n",
      "Epoch: 33, Iter: 200, Loss D: 0.851047, Loss G: 1.348427\n",
      "Epoch: 33, Iter: 300, Loss D: 0.758851, Loss G: 1.414840\n",
      "Epoch: 33, Iter: 400, Loss D: 0.833104, Loss G: 2.343185\n",
      "Epoch: 33, Iter: 500, Loss D: 0.893344, Loss G: 1.581725\n",
      "Epoch: 33, Iter: 600, Loss D: 0.667053, Loss G: 1.754735\n",
      "Epoch: 33, Iter: 700, Loss D: 0.863168, Loss G: 1.487753\n",
      "Epoch: 33, Iter: 800, Loss D: 0.836241, Loss G: 2.023566\n",
      "Epoch: 33, Iter: 900, Loss D: 0.722988, Loss G: 2.186960\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 34, Iter: 0, Loss D: 0.720861, Loss G: 2.085377\n",
      "Epoch: 34, Iter: 100, Loss D: 0.664193, Loss G: 2.044673\n",
      "Epoch: 34, Iter: 200, Loss D: 0.668159, Loss G: 1.780181\n",
      "Epoch: 34, Iter: 300, Loss D: 0.849270, Loss G: 1.501506\n",
      "Epoch: 34, Iter: 400, Loss D: 0.743617, Loss G: 2.149403\n",
      "Epoch: 34, Iter: 500, Loss D: 0.715390, Loss G: 1.764290\n",
      "Epoch: 34, Iter: 600, Loss D: 0.802526, Loss G: 1.620236\n",
      "Epoch: 34, Iter: 700, Loss D: 0.776149, Loss G: 2.258041\n",
      "Epoch: 34, Iter: 800, Loss D: 0.714807, Loss G: 2.175318\n",
      "Epoch: 34, Iter: 900, Loss D: 0.727366, Loss G: 1.940960\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 35, Iter: 0, Loss D: 0.816226, Loss G: 1.697927\n",
      "Epoch: 35, Iter: 100, Loss D: 0.671767, Loss G: 2.511909\n",
      "Epoch: 35, Iter: 200, Loss D: 0.884969, Loss G: 2.637171\n",
      "Epoch: 35, Iter: 300, Loss D: 0.746095, Loss G: 1.682825\n",
      "Epoch: 35, Iter: 400, Loss D: 1.035603, Loss G: 2.581606\n",
      "Epoch: 35, Iter: 500, Loss D: 0.730516, Loss G: 2.002112\n",
      "Epoch: 35, Iter: 600, Loss D: 0.733109, Loss G: 1.804167\n",
      "Epoch: 35, Iter: 700, Loss D: 0.788976, Loss G: 2.284079\n",
      "Epoch: 35, Iter: 800, Loss D: 0.928262, Loss G: 2.281459\n",
      "Epoch: 35, Iter: 900, Loss D: 0.559877, Loss G: 1.810120\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 36, Iter: 0, Loss D: 1.103835, Loss G: 1.739978\n",
      "Epoch: 36, Iter: 100, Loss D: 0.758652, Loss G: 2.157750\n",
      "Epoch: 36, Iter: 200, Loss D: 0.964222, Loss G: 1.724133\n",
      "Epoch: 36, Iter: 300, Loss D: 0.770826, Loss G: 2.085097\n",
      "Epoch: 36, Iter: 400, Loss D: 0.674940, Loss G: 2.064342\n",
      "Epoch: 36, Iter: 500, Loss D: 0.816309, Loss G: 1.912948\n",
      "Epoch: 36, Iter: 600, Loss D: 0.809446, Loss G: 2.272217\n",
      "Epoch: 36, Iter: 700, Loss D: 1.092239, Loss G: 2.018727\n",
      "Epoch: 36, Iter: 800, Loss D: 0.817127, Loss G: 2.562539\n",
      "Epoch: 36, Iter: 900, Loss D: 0.972972, Loss G: 1.691296\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 37, Iter: 0, Loss D: 0.929664, Loss G: 1.634748\n",
      "Epoch: 37, Iter: 100, Loss D: 0.552830, Loss G: 1.819595\n",
      "Epoch: 37, Iter: 200, Loss D: 0.713799, Loss G: 2.058564\n",
      "Epoch: 37, Iter: 300, Loss D: 0.785997, Loss G: 1.663087\n",
      "Epoch: 37, Iter: 400, Loss D: 0.769178, Loss G: 1.943559\n",
      "Epoch: 37, Iter: 500, Loss D: 1.019863, Loss G: 1.861024\n",
      "Epoch: 37, Iter: 600, Loss D: 0.638354, Loss G: 2.022588\n",
      "Epoch: 37, Iter: 700, Loss D: 0.761158, Loss G: 2.246675\n",
      "Epoch: 37, Iter: 800, Loss D: 0.752822, Loss G: 2.074193\n",
      "Epoch: 37, Iter: 900, Loss D: 0.738434, Loss G: 2.629432\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 38, Iter: 0, Loss D: 0.719243, Loss G: 1.913049\n",
      "Epoch: 38, Iter: 100, Loss D: 0.747607, Loss G: 1.756668\n",
      "Epoch: 38, Iter: 200, Loss D: 0.921465, Loss G: 1.611985\n",
      "Epoch: 38, Iter: 300, Loss D: 0.860991, Loss G: 1.980564\n",
      "Epoch: 38, Iter: 400, Loss D: 0.722550, Loss G: 1.921545\n",
      "Epoch: 38, Iter: 500, Loss D: 0.903116, Loss G: 1.815550\n",
      "Epoch: 38, Iter: 600, Loss D: 0.997324, Loss G: 2.598338\n",
      "Epoch: 38, Iter: 700, Loss D: 0.881067, Loss G: 1.193837\n",
      "Epoch: 38, Iter: 800, Loss D: 0.625927, Loss G: 2.160597\n",
      "Epoch: 38, Iter: 900, Loss D: 0.887262, Loss G: 2.098822\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 39, Iter: 0, Loss D: 0.854081, Loss G: 3.199675\n",
      "Epoch: 39, Iter: 100, Loss D: 0.961785, Loss G: 2.254215\n",
      "Epoch: 39, Iter: 200, Loss D: 0.682430, Loss G: 2.346254\n",
      "Epoch: 39, Iter: 300, Loss D: 0.793694, Loss G: 1.721635\n",
      "Epoch: 39, Iter: 400, Loss D: 0.639752, Loss G: 2.363258\n",
      "Epoch: 39, Iter: 500, Loss D: 0.631129, Loss G: 1.583138\n",
      "Epoch: 39, Iter: 600, Loss D: 0.845459, Loss G: 2.325561\n",
      "Epoch: 39, Iter: 700, Loss D: 0.786223, Loss G: 2.000709\n",
      "Epoch: 39, Iter: 800, Loss D: 0.756143, Loss G: 2.041603\n",
      "Epoch: 39, Iter: 900, Loss D: 0.857041, Loss G: 2.071381\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 40, Iter: 0, Loss D: 0.745480, Loss G: 1.957735\n",
      "Epoch: 40, Iter: 100, Loss D: 0.715202, Loss G: 1.804412\n",
      "Epoch: 40, Iter: 200, Loss D: 0.875807, Loss G: 1.397153\n",
      "Epoch: 40, Iter: 300, Loss D: 0.775577, Loss G: 2.055773\n",
      "Epoch: 40, Iter: 400, Loss D: 0.754591, Loss G: 2.839309\n",
      "Epoch: 40, Iter: 500, Loss D: 0.899492, Loss G: 2.088295\n",
      "Epoch: 40, Iter: 600, Loss D: 0.865490, Loss G: 1.814920\n",
      "Epoch: 40, Iter: 700, Loss D: 0.816688, Loss G: 1.926199\n",
      "Epoch: 40, Iter: 800, Loss D: 0.785078, Loss G: 2.263569\n",
      "Epoch: 40, Iter: 900, Loss D: 0.781438, Loss G: 2.016876\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 41, Iter: 0, Loss D: 0.891929, Loss G: 2.050470\n",
      "Epoch: 41, Iter: 100, Loss D: 0.671579, Loss G: 2.397668\n",
      "Epoch: 41, Iter: 200, Loss D: 0.933851, Loss G: 1.878026\n",
      "Epoch: 41, Iter: 300, Loss D: 0.650860, Loss G: 2.258699\n",
      "Epoch: 41, Iter: 400, Loss D: 0.732476, Loss G: 2.902382\n",
      "Epoch: 41, Iter: 500, Loss D: 0.827672, Loss G: 1.510877\n",
      "Epoch: 41, Iter: 600, Loss D: 0.965522, Loss G: 1.796472\n",
      "Epoch: 41, Iter: 700, Loss D: 0.671802, Loss G: 1.654322\n",
      "Epoch: 41, Iter: 800, Loss D: 0.769386, Loss G: 2.811571\n",
      "Epoch: 41, Iter: 900, Loss D: 0.857832, Loss G: 2.096262\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 42, Iter: 0, Loss D: 0.760054, Loss G: 2.631384\n",
      "Epoch: 42, Iter: 100, Loss D: 0.727308, Loss G: 2.387344\n",
      "Epoch: 42, Iter: 200, Loss D: 0.686597, Loss G: 2.233560\n",
      "Epoch: 42, Iter: 300, Loss D: 0.675453, Loss G: 1.811741\n",
      "Epoch: 42, Iter: 400, Loss D: 0.900549, Loss G: 1.930949\n",
      "Epoch: 42, Iter: 500, Loss D: 0.820935, Loss G: 2.087671\n",
      "Epoch: 42, Iter: 600, Loss D: 0.860668, Loss G: 2.139052\n",
      "Epoch: 42, Iter: 700, Loss D: 0.815016, Loss G: 2.549263\n",
      "Epoch: 42, Iter: 800, Loss D: 0.725780, Loss G: 1.821742\n",
      "Epoch: 42, Iter: 900, Loss D: 0.771082, Loss G: 1.918962\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 43, Iter: 0, Loss D: 0.890943, Loss G: 1.577055\n",
      "Epoch: 43, Iter: 100, Loss D: 0.674633, Loss G: 1.776289\n",
      "Epoch: 43, Iter: 200, Loss D: 0.861254, Loss G: 2.834362\n",
      "Epoch: 43, Iter: 300, Loss D: 0.820190, Loss G: 1.760918\n",
      "Epoch: 43, Iter: 400, Loss D: 0.620186, Loss G: 2.316655\n",
      "Epoch: 43, Iter: 500, Loss D: 0.792100, Loss G: 2.065020\n",
      "Epoch: 43, Iter: 600, Loss D: 0.550246, Loss G: 2.736711\n",
      "Epoch: 43, Iter: 700, Loss D: 0.787638, Loss G: 1.664877\n",
      "Epoch: 43, Iter: 800, Loss D: 0.725854, Loss G: 2.045885\n",
      "Epoch: 43, Iter: 900, Loss D: 0.849366, Loss G: 2.642249\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 44, Iter: 0, Loss D: 0.628751, Loss G: 2.514839\n",
      "Epoch: 44, Iter: 100, Loss D: 0.791720, Loss G: 2.098617\n",
      "Epoch: 44, Iter: 200, Loss D: 0.776784, Loss G: 2.383003\n",
      "Epoch: 44, Iter: 300, Loss D: 0.732915, Loss G: 2.894552\n",
      "Epoch: 44, Iter: 400, Loss D: 0.698833, Loss G: 1.942444\n",
      "Epoch: 44, Iter: 500, Loss D: 0.826105, Loss G: 1.758396\n",
      "Epoch: 44, Iter: 600, Loss D: 0.678513, Loss G: 2.435884\n",
      "Epoch: 44, Iter: 700, Loss D: 0.741676, Loss G: 2.295468\n",
      "Epoch: 44, Iter: 800, Loss D: 0.672127, Loss G: 2.040155\n",
      "Epoch: 44, Iter: 900, Loss D: 0.767576, Loss G: 2.179025\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 45, Iter: 0, Loss D: 0.853780, Loss G: 2.513367\n",
      "Epoch: 45, Iter: 100, Loss D: 0.814905, Loss G: 2.301954\n",
      "Epoch: 45, Iter: 200, Loss D: 0.885371, Loss G: 1.949178\n",
      "Epoch: 45, Iter: 300, Loss D: 0.710172, Loss G: 1.975971\n",
      "Epoch: 45, Iter: 400, Loss D: 0.787378, Loss G: 1.931849\n",
      "Epoch: 45, Iter: 500, Loss D: 0.667900, Loss G: 2.148268\n",
      "Epoch: 45, Iter: 600, Loss D: 0.666490, Loss G: 1.801171\n",
      "Epoch: 45, Iter: 700, Loss D: 0.766456, Loss G: 2.429586\n",
      "Epoch: 45, Iter: 800, Loss D: 0.862604, Loss G: 1.776797\n",
      "Epoch: 45, Iter: 900, Loss D: 0.682128, Loss G: 1.757885\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 46, Iter: 0, Loss D: 0.721570, Loss G: 2.175990\n",
      "Epoch: 46, Iter: 100, Loss D: 0.591694, Loss G: 2.740356\n",
      "Epoch: 46, Iter: 200, Loss D: 0.663061, Loss G: 1.652085\n",
      "Epoch: 46, Iter: 300, Loss D: 0.857418, Loss G: 2.981510\n",
      "Epoch: 46, Iter: 400, Loss D: 0.831338, Loss G: 3.163899\n",
      "Epoch: 46, Iter: 500, Loss D: 0.981467, Loss G: 2.072440\n",
      "Epoch: 46, Iter: 600, Loss D: 0.726899, Loss G: 1.501190\n",
      "Epoch: 46, Iter: 700, Loss D: 0.796270, Loss G: 2.221293\n",
      "Epoch: 46, Iter: 800, Loss D: 0.942597, Loss G: 2.142768\n",
      "Epoch: 46, Iter: 900, Loss D: 0.648078, Loss G: 1.962093\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 47, Iter: 0, Loss D: 0.655447, Loss G: 2.422470\n",
      "Epoch: 47, Iter: 100, Loss D: 0.775214, Loss G: 2.516680\n",
      "Epoch: 47, Iter: 200, Loss D: 0.766249, Loss G: 2.109856\n",
      "Epoch: 47, Iter: 300, Loss D: 0.711484, Loss G: 2.054706\n",
      "Epoch: 47, Iter: 400, Loss D: 0.638630, Loss G: 2.161673\n",
      "Epoch: 47, Iter: 500, Loss D: 0.813567, Loss G: 2.027385\n",
      "Epoch: 47, Iter: 600, Loss D: 0.803991, Loss G: 2.633614\n",
      "Epoch: 47, Iter: 700, Loss D: 0.883243, Loss G: 2.076679\n",
      "Epoch: 47, Iter: 800, Loss D: 0.803860, Loss G: 2.509020\n",
      "Epoch: 47, Iter: 900, Loss D: 0.717610, Loss G: 1.963725\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 48, Iter: 0, Loss D: 0.987451, Loss G: 2.190319\n",
      "Epoch: 48, Iter: 100, Loss D: 0.844858, Loss G: 2.056426\n",
      "Epoch: 48, Iter: 200, Loss D: 0.950566, Loss G: 2.349670\n",
      "Epoch: 48, Iter: 300, Loss D: 0.814586, Loss G: 2.708252\n",
      "Epoch: 48, Iter: 400, Loss D: 0.664275, Loss G: 2.689126\n",
      "Epoch: 48, Iter: 500, Loss D: 0.807372, Loss G: 1.530454\n",
      "Epoch: 48, Iter: 600, Loss D: 0.897264, Loss G: 3.126796\n",
      "Epoch: 48, Iter: 700, Loss D: 0.766525, Loss G: 2.379807\n",
      "Epoch: 48, Iter: 800, Loss D: 0.822497, Loss G: 2.290273\n",
      "Epoch: 48, Iter: 900, Loss D: 0.642993, Loss G: 1.692631\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n",
      "Epoch: 49, Iter: 0, Loss D: 0.792924, Loss G: 2.744007\n",
      "Epoch: 49, Iter: 100, Loss D: 0.750447, Loss G: 2.253338\n",
      "Epoch: 49, Iter: 200, Loss D: 0.561862, Loss G: 1.936717\n",
      "Epoch: 49, Iter: 300, Loss D: 0.948560, Loss G: 2.803340\n",
      "Epoch: 49, Iter: 400, Loss D: 0.876370, Loss G: 1.060065\n",
      "Epoch: 49, Iter: 500, Loss D: 0.772983, Loss G: 2.138982\n",
      "Epoch: 49, Iter: 600, Loss D: 0.735304, Loss G: 2.092630\n",
      "Epoch: 49, Iter: 700, Loss D: 0.707748, Loss G: 1.943646\n",
      "Epoch: 49, Iter: 800, Loss D: 0.589765, Loss G: 1.758055\n",
      "Epoch: 49, Iter: 900, Loss D: 0.723670, Loss G: 1.579351\n",
      "Learning Rate (Generator): 0.0001\n",
      "Learning Rate (Discriminator): 0.0002\n"
     ]
    }
   ],
   "source": [
    "generator = Generator(100).to(device)\n",
    "discriminator = Discriminator().to(device)\n",
    "adversarial = Adversarial(generator, discriminator).to(device)\n",
    "batch_size = 64\n",
    "epochs = 50\n",
    "train(generator, discriminator, adversarial, \n",
    "      train_dataloader, epochs=epochs)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generate an Animated GIF \n",
    "\n",
    "To see the evolution of the generated samples, we will generate an animated gif of all the images saved during training.\n",
    "\n",
    "Install     imageio first:\n",
    "\n",
    "`pip install imageio`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/gif": "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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m/home/rowel/github/roatienza/Deep-Learning-Experiments/versions/2023/gan/demo/dcgan_mnist.ipynb Cell 17\u001b[0m line \u001b[0;36m2\n\u001b[1;32m     <a href='vscode-notebook-cell://ssh-remote%2B202.92.132.48/home/rowel/github/roatienza/Deep-Learning-Experiments/versions/2023/gan/demo/dcgan_mnist.ipynb#X21sdnNjb2RlLXJlbW90ZQ%3D%3D?line=21'>22</a>\u001b[0m clear_output(wait\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[1;32m     <a href='vscode-notebook-cell://ssh-remote%2B202.92.132.48/home/rowel/github/roatienza/Deep-Learning-Experiments/versions/2023/gan/demo/dcgan_mnist.ipynb#X21sdnNjb2RlLXJlbW90ZQ%3D%3D?line=22'>23</a>\u001b[0m display(Image(filename\u001b[39m=\u001b[39moutput_filename))\n\u001b[0;32m---> <a href='vscode-notebook-cell://ssh-remote%2B202.92.132.48/home/rowel/github/roatienza/Deep-Learning-Experiments/versions/2023/gan/demo/dcgan_mnist.ipynb#X21sdnNjb2RlLXJlbW90ZQ%3D%3D?line=23'>24</a>\u001b[0m time\u001b[39m.\u001b[39;49msleep(\u001b[39m5\u001b[39;49m)\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import imageio\n",
    "import time\n",
    "from IPython.display import Image\n",
    "from IPython.display import display\n",
    "from IPython.display import clear_output\n",
    "\n",
    "# List of PNG image filenames\n",
    "image_filenames = [f\"gan_ep-{i}.png\" for i in range(epochs)]\n",
    "\n",
    "# Read the PNG images and append them to a list\n",
    "images = []\n",
    "for filename in image_filenames:\n",
    "    image = imageio.imread(filename)\n",
    "    images.append(image)\n",
    "\n",
    "# Save the list of images as a GIF\n",
    "output_filename = 'dcgan_anim.gif'\n",
    "imageio.mimsave(output_filename, images, duration=0.5)\n",
    "\n",
    "# Display the GIF\n",
    "while True:\n",
    "    clear_output(wait=True)\n",
    "    display(Image(filename=output_filename))\n",
    "    time.sleep(2)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "speech",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
